In recent years, the health care community has made an effort to adopt electronic documentation of medical records. Electronic clinical documentation can provide direct benefits in efficiency and accuracy of patient treatment, can reduce costs associated with coding and billing, and can improve management and administration of medical resources and personnel. Thus, use of electronic documentation can improve the effectiveness of many aspects of the healthcare system. However, conventional electronic clinical documentation solutions have a poor track record of actually being used by health care providers. For example, some research shows that electronic formats account for only a small percentage of all clinical documentation. One reason for this low level of use is that doctors report as much as a 50% increase in time spent preparing documentation following the adoption of electronic documentation systems.
Traditional clinical documentation is typically a “linear input” user experience in which a health care provider starts to write a document, and when he or she wants to reference an external data source, such as a lab report, the health care provider saves a draft of the document, jumps to a lab module, remembers or copies particular data from the lab report, goes back to the document, and enters or pastes the lab data into the document. This process may be repeated a number of times during preparation of a document, can be very time consuming, and may introduce errors caused by human mistake. Thus, conventional systems may allow a user to copy and paste data into documentation, but the user may have to jump back and forth through different modules, which risks losing the context of the current input. Further, some conventional electronic documentation systems use macros to retrieve data from other systems for inputting the data into clinical documentation, but users can have difficulties remembering all of the macros and/or harmonizing new data into a current document.
Another challenge in electronic clinical documentation is providing a hybrid data entry mode for enabling input of both structured data and unstructured data into a document. For example, a diagnosis may contain a large number of medical terms, and typically each of these terms may be defined in SNOMED CT® (Systematized Nomenclature of Medicine—Clinical Terms). In conventional electronic documentation solutions, a care provider may have a difficult time identifying each SNOMED-defined term and inputting the term into the electronic documentation system, and thus, might leave some terms out. However, providing a complete and accurate record may be important for correct diagnosis, treatment and the like. Further, while there has been a large amount of research related to the use of natural language processing (NLP) in electronic document creation, NLP is typically not widely used in a clinical environment because even a small error rate in word recognition may cause a serious patient safety issue and/or may contribute to lowered efficiency because the health care provider may have to carefully proofread each entry to protect against errors.